Analysis of Prognostic Factors of Nasopharyngeal Carcinoma and Construction of Risk Nomogram Prediction Model

Abstract Objective To analyze the factors affecting the prognosis of nasopharyngeal carcinoma (NPC) patients and establish a risk nomogram prediction model for the diagnosis, treatment and prognosis of NPC. Methods A total of 1645 NPC patients (1470 survival) from 1973 to 2015 in the SEER database were included. The clinicopathological data of 180 NPC patients hospitalized in the First Affiliated Hospital of Bengbu Medical College since 2010 were collected. The spearman correlation analysis and COX regression analysis were used to identify the prognostic factors of NPC and establish the risk nomogram prediction model. Receiver operation characteristic (ROC) analysis and area under curves (AUC) were used to evaluate the ability of distinguishing the prognosis of NPC patients. The cross calibration was calculated to evaluate the external validation of established predictive model.Results The survival NPC had higher proportion in female, older age, degree of NPC differentiation, N and M stage (all P<0.05. The study factors including female, older age, and higher degree of differentiation were positively correlated with the prognosis of NPC patients (P<0.05), while N and M stage were negatively correlated with the prognosis of NPC patients (P<0.05). The factors including female (HR=4.92, 95%CI 3.58-6.75), age (HR=, 95%CI=), degree of differentiation (HR=, 95%CI=), N stage (HR=, 95%CI=) were independently associated with prognosis of NPC patients (P<0.05). Comparing with calibration curve, the constructed risk prediction model had good relations between predictive values and actual values (AUC=0.833). Additionally, the external verification results of 96 patients with NPC had a larger AUC in our established predictive model (AUC=0.851). Conclusions Female, degree of differentiation and N stage are independent factors for the prognosis of NPC patients. The risk nomogram prediction model has a good predictive ability for the survival of NPC patients, which can provide important insights into clinical prognosis for NPC patients.

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A prediction model for degree of differentiation for resectable locally advanced esophageal squamous cell carcinoma based on CT images using radiomics and machine-learning

British Journal of Radiology ◽

10.1259/bjr.20210525 ◽

2021 ◽

pp. 20210525

Author(s):

Daisuke Kawahara ◽

Yuji Murakami ◽

Shigeyuki Tani ◽

Yasushi Nagata

Keyword(s):

Machine Learning ◽

Squamous Cell Carcinoma ◽

Esophageal Cancer ◽

Prediction Model ◽

Cell Carcinoma ◽

Predictive Model ◽

Squamous Cell ◽

Locally Advanced ◽

Degree Of Differentiation ◽

Planning Ct

Objective: To propose the prediction model for degree of differentiation for locally advanced esophageal cancer patients from the planning CT image by radiomics analysis with machine learning. Methods: Data of 104 patients with esophagus cancer, who underwent chemoradiotherapy followed by surgery at the Hiroshima University hospital from 2003 to 2016 were analyzed. The treatment outcomes of these tumors were known prior to the study. The data were split into 3 sets: 57/16 tumors for the training/validation and 31 tumors for model testing. The degree of differentiation of squamous cell carcinoma was classified into two groups. The first group (Group I) was a poorly differentiated (POR) patients. The second group (Group II) was well and moderately differentiated patients. The radiomics feature was extracted in the tumor and around the tumor regions. A total number of 3480 radiomics features per patient image were extracted from radiotherapy planning CT scan. Models were built with the least absolute shrinkage and selection operator (LASSO) logistic regression and applied to the set of candidate predictors. The radiomics features were used for the input data in the machine learning. To build predictive models with radiomics features, neural network classifiers was used. The precision, accuracy, sensitivity by generating confusion matrices, the area under the curve (AUC) of receiver operating characteristic curve were evaluated. Results: By the LASSO analysis of the training data, we found 13 radiomics features from CT images for the classification. The accuracy of the prediction model was highest for using only CT radiomics features. The accuracy, specificity, and sensitivity of the predictive model were 85.4%, 88.6%, 80.0%, and the AUC was 0.92. Conclusion: The proposed predictive model showed high accuracy for the classification of the degree of the differentiation of esophagus cancer. Because of the good prediction ability of the method, the method may contribute to reducing the pathological examination by biopsy and predicting the local control. Advances in knowledge: For esophageal cancer, the differentiation of degree is the import indexes reflecting the aggressiveness. The current study proposed the prediction model for the differentiation of degree with radiomics analysis.

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Hematological Indexes Can Be Used to Predict the Incidence of Hypothyroidism in Nasopharyngeal Carcinoma Patients after Radiotherapy

BioMed Research International ◽

10.1155/2020/3860936 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Ling Zhou ◽

Jia Chen ◽

Chang-Juan Tao ◽

Shuang Huang ◽

Jiang Zhang ◽

...

Keyword(s):

Nasopharyngeal Carcinoma ◽

Predictive Model ◽

Immune Cells ◽

Cross Validation ◽

Complete Blood Count ◽

Univariate Analysis ◽

Optimal Method ◽

Internal Validation ◽

N Stage ◽

Tenfold Cross Validation

Background. This study explored the relationship between thyroid-associated antibodies, immune cells, and hypothyroidism to establish a predictive model for the incidence of hypothyroidism in patients with nasopharyngeal carcinoma (NPC) after radiotherapy. Methods. A total of 170 patients with NPC treated at the Cancer Hospital of University of Chinese Academy of Sciences between January 2015 and August 2018 were included. The complete blood count, biochemical, coagulation function, immune cells, and thyroid-associated antibodies tested before radiotherapy were evaluated. A logistic regression model was performed to elucidate which hematological indexes were related to hypothyroidism development. A predictive model for the incidence of hypothyroidism was established. Internal verification of the multifactor model was performed using the tenfold cross-validation method. Results. The univariate analysis showed that immune cells had no statistically significant differences among the patients with and without hypothyroidism. Sex, N-stage, antithyroid peroxidase antibody (TPO-Ab), antithyroglobulin antibody (TG-Ab), thyroglobulin (TG), and fibrinogen (Fb) were associated with hypothyroidism. Males and early N-stage were protective factors of thyroid function, whereas increases in TPO-Ab, TG-Ab, TG, and Fb counts were associated with an increased rate of hypothyroidism incidence. The multivariate analysis showed that TPO-Ab, TG-Ab, TG, and Fb were independent predictors of hypothyroidism. The comprehensive effect of the significant model, including TPO-Ab, TG-Ab, TG, and Fb counts, represented the optimal method of predicting the incidence of radiation-induced hypothyroidism (AUC=0.796). Tenfold cross-validation methods were applied for internal validation. The AUCs of the training and testing sets were 0.792 and 0.798, respectively. Conclusion. A model combining TPO-Ab, TG-Ab, TG, and Fb can be used to screen populations at a high risk of developing hypothyroidism after radiotherapy.

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Development and Validation of Nomograms to Predict Prognosis of Oral and Oropharyngeal Mucoepidermoid Carcinoma: A SEER-Based Study

10.21203/rs.3.rs-125136/v1 ◽

2020 ◽

Author(s):

muyuan liu ◽

Litian Tong ◽

Manbin Xu ◽

Xiang Xu ◽

Bin Liang ◽

...

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Mucoepidermoid Carcinoma ◽

Cox Regression ◽

Seer Database ◽

Disease Specific Survival ◽

Cox Regression Analysis ◽

T Stage ◽

N Stage ◽

Disease Specific

Abstract Background: Due to the low incidence of mucoepidermoid carcinoma, there lacks sufficient studies for determining optimal treatment and predicting prognosis. The purpose of this study was to develop prognostic nomograms, to predict overall survival and disease-specific survival (DSS) of oral and oropharyngeal mucoepidermoid carcinoma patients, using the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) database. Methods: Clinicopathological and follow-up data of patients diagnosed with oral and oropharyngeal mucoepidermoid carcinoma between 2004 and 2017 were collected from the SEER database. The Kaplan-Meier method with the log-rank test was employed to identify single prognostic factors. Multivariate Cox regression was utilized to identify independent prognostic factors. C-index, area under the ROC curve (AUC) and calibration curves were used to assess performance of the prognostic nomograms. Results: A total of 1230 patients with oral and oropharyngeal mucoepidermoid carcinoma were enrolled in the present study. After multivariate Cox regression analysis, age, sex, tumor subsite, T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for overall survival. T stage, N stage, M stage, grade and surgery were identified as independent prognostic factors for disease-specific survival. Nomograms were constructed to predict the overall survival and disease-specific survival based on the independent prognostic factors. The fitted nomograms possessed excellent prediction accuracy, with a C-index of 0.899 for OS prediction and 0.893 for DSS prediction. Internal validation by computing the bootstrap calibration plots, using the validation set, indicated excellent performance by the nomograms. Conclusion: The prognostic nomograms developed, based on individual clinicopathological characteristics, in the present study, accurately predicted the overall survival and disease-specific survival of patients with oral and oropharyngeal mucoepidermoid carcinoma.

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Incidence and prognostic significance of extramural venous invasion in T3-4 esophageal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.4_suppl.44 ◽

2017 ◽

Vol 35 (4_suppl) ◽

pp. 44-44

Author(s):

Zohra Faiz ◽

Lotte Huijgen ◽

Hanadi Alqethami ◽

Gursah Kats-Ugurlu ◽

John Theodorus Plukker

Keyword(s):

Esophageal Cancer ◽

Prognostic Factors ◽

Squamous Cell ◽

Prognostic Significance ◽

Disease Free Survival ◽

Venous Invasion ◽

Free Survival ◽

Cox Regression Analysis ◽

N Stage ◽

Disease Free

44 Background: Extra mural venous invasion (EMVI) is known as an adverse prognostic indicator of survival and an important predictor of systemic recurrences in colorectal carcinoma. However, the incidence and significance of EMVI in esophageal cancer (EC) have not been studied well. Aim: To estimate the incidence and the prognostic significance of EMVI in pathological stage T3-4 EC resection specimens without neoadjuvant chemoradiotherapy (nCRT) Methods: From a prospective maintained database we retrospectively review the resected specimens of patients with an EC. For adequate EMVI assessment we only included patients without nCRT and excluded patients without previous malignancies and patients with cardia/gastroesophageal junction tumors. A total of 76 patients were included between 2000 and 2012. EMVI was assessed on haematoxylin and eosin slides and for confirmation additional Elastica van Gieson staining was performed. Disease free survival was analyzed using a multivariable logistic regression and Kaplan-Meier method. Results: Evidence of EMVI was found in 18 patients (23.7%). The incidence of EMVI was significantly higher in squamous cell carcinomas (p < 0.05) and among tumors located in the mid-esophagus (p < 0.05). In the univariate analysis, pathological N- stage (HR 3.6 95%Cl: 1.5-8.5; p = 0.004) and positive circumferential resection margin (CRM) (HR 2.3 95% Cl: 1.2-4.5 p = 0.015) were independent prognostic factors. By stepwise multivariate Cox regression analysis, pathological tumor length (HR 2.5 95%Cl: 1.0-6.5 p = 0.05), pathological N- stage (HR 2.7 95%Cl: 1.3-6.2 p = 0.008), positive CRM (HR 2.3 95% Cl: 1.2-4.5 p = 0.009) and EMVI (HR 2.2 95% Cl: 1.0-4.5 p = 0.04) were shown to be independent prognostic factors for disease free survival. Conclusions: A search for EMVI may provide additional prognostic information in esophageal cancer, especially in squamous cell carcinoma.

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Early atypical signs and insula hypometabolism predict survival in multiple system atrophy

Journal of Neurology Neurosurgery & Psychiatry ◽

10.1136/jnnp-2020-324823 ◽

2021 ◽

pp. jnnp-2020-324823

Author(s):

Stephan Grimaldi ◽

Mohamed Boucekine ◽

Tatiana Witjas ◽

Frederique Fluchere ◽

Jean-Philippe Azulay ◽

...

Keyword(s):

Prognostic Factors ◽

Multiple System Atrophy ◽

Median Survival ◽

Risk Score ◽

Cox Regression ◽

Cox Model ◽

Clinical Prognosis ◽

Cox Regression Analysis ◽

Brain Pet ◽

Left Insula

ObjectiveWe aim to search for predictors of survival among clinical and brain 18F-FDG positron emission tomography (PET) metabolic features in our cohort of patients with multiple system atrophy (MSA).MethodsWe included patients with a ‘probable’ MSA diagnosis for whom a clinical evaluation and a brain PET were performed early in the course of the disease (median 3 years, IQR 2–5). A retrospective analysis was conducted using standardised data collection. Brain PET metabolism was characterised using the Automated Anatomical Labelling Atlas. A Cox model was applied to look for factors influencing survival. Kaplan-Meier method estimated the survival rate. We proposed to develop a predictive ‘risk score’, categorised into low-risk and high-risk groups, using significant variables entered in multivariate Cox regression analysis.ResultsEighty-five patients were included. The overall median survival was 8 years (CI 6.64 to 9.36). Poor prognostic factors were orthostatic hypotension (HR=6.04 (CI 1.58 to 23.12), p=0.009), stridor (HR=3.41 (CI 1.31 to 8.87), p=0.012) and glucose PET hypometabolism in the left insula (HR=0.78 (CI 0.66 to 0.92), p=0.004). Good prognostic factors were time to diagnosis (HR=0.68 (CI 0.54 to 0.86), p=0.001) and use of selective serotonin reuptake inhibitor (SSRI) (HR=0.17 (CI 0.06 to 0.46), p<0.001). The risk score revealed a 5-year gap separating the median survival of the two groups obtained (5 years vs 10 years; HR=5.82 (CI 2.94 to 11.49), p<0.001).ConclusionThe clinical prognosis factors we have described support published studies. Here, we also suggest that brain PET is of interest for prognosis assessment and in particular in the search for left insula hypometabolism. Moreover, SSRIs are a potential drug candidate to slow the progression of the disease.

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Role of the TGFβ/PDCD4/AP-1 Signaling Pathway in Nasopharyngeal Carcinoma and Its Relationship to Prognosis

Cellular Physiology and Biochemistry ◽

10.1159/000481871 ◽

2017 ◽

Vol 43 (4) ◽

pp. 1392-1401 ◽

Cited By ~ 6

Author(s):

Jie Ma ◽

Shu-Hong Xuan ◽

Yan Li ◽

Zhi-Ping Zhang ◽

Xin-Hua Li

Keyword(s):

Regression Analysis ◽

Prognostic Factors ◽

Nasopharyngeal Carcinoma ◽

Cox Regression ◽

Advanced Tumor Stage ◽

Cox Regression Analysis ◽

Normal Tissues ◽

Kaplan Meier ◽

Low Expression

Background: The objective of the present study was to evaluate the role of the TGFβ/PDCD4/AP-1 pathway in nasopharyngeal carcinoma (NPC) and its relationship to NPC prognosis. Methods: NPC tissues collected from 66 NPC patients were compared to 17 nasopharyngeal mucosa biopsy specimens collected as normal tissues. Immunohistochemical staining was performed to assess expression of transforming growth factor-β receptor I (TGFβRI), programmed cell death 4 (PDCD4) and activator protein-1 (AP-1). The Kaplan-Meier method was applied to evaluate NPC patient overall survival (OS) and progression-free-survival (PFS). Cox regression analysis was used to estimate independent prognostic factors for NPC. The human NPC cell line CNE2 was selected and treated with SB431542, an inhibitor of TGFβRI; expression of TGFβRI and PDCD4 in CNE2 cells was determined by western blotting. NPC tissues showed higher expression of TGFβRI and AP-1 but lower expression of PDCD4 than normal tissues (all P < 0.05). Results: The results of Kaplan-Meier analysis showed that TGFβRI-positive patients and AP-1-positive patients had shorter OS and PFS than TGFβRI-negative patients and AP-1-negative patients; additionally, PDCD4-positive patients had higher OS and PFS than PDCD4-negative patients. Cox regression analysis revealed that advanced tumor stage, overexpression of TGFβRI and AP-1, and low expression of PDCD4 were unfavorable factors influencing OS and PFS in NPC patients. Compared with the control group, expression of TGFβRI decreased and that of PDCD4 increased significantly in CNE2 cells treated with the inhibitor (all P < 0.05). These findings indicate that the TGFβ/PDCD4/AP-1 pathway may be associated with NPC development and progression. Conclusion: High expression of TGFβRI and AP-1 and low expression of PDCD4 may be unfavorable prognostic factors for NPC.

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Systemic Inflammation Response Index Is a Predictor of Poor Survival in Locally Advanced Nasopharyngeal Carcinoma: A Propensity Score Matching Study

Frontiers in Oncology ◽

10.3389/fonc.2020.575417 ◽

2020 ◽

Vol 10 ◽

Author(s):

Yuhua Feng ◽

Na Zhang ◽

Sisi Wang ◽

Wen Zou ◽

Yan He ◽

...

Keyword(s):

Prognostic Factors ◽

Nasopharyngeal Carcinoma ◽

Propensity Score ◽

Propensity Score Matching ◽

Systemic Inflammation ◽

Locally Advanced ◽

Cox Regression Analysis ◽

Inflammation Response ◽

Lymphocyte Ratio ◽

Response Index

IntroductionNasopharyngeal carcinoma (NPC) is a common malignancy in China and known prognostic factors are limited. In this study, neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune inflammation index (SII), and systemic inflammation response index (SIRI) were evaluated as prognostic factors in locally advanced NPC patients.Materials and MethodsNPC patients who received curative radiation or chemoradiation between January 2012 and December 2015 at the Second Xiangya Hospital were retrospectively reviewed, and a total of 516 patients were shortlisted. After propensity score matching (PSM), 417 patients were eventually enrolled. Laboratory and clinical data were collected from the patients’ records. Receiver operating characteristic curve analysis was used to determine the optimal cut-off value. Survival curves were analyzed using the Kaplan-Meier method. The Cox proportional hazard model was used to identify prognostic variables.ResultsAfter PSM, all basic characteristics between patients in the high SIRI group and low SIRI group were balanced except for sex (p=0.001) and clinical stage (p=0.036). Univariate analysis showed that NLR (p=0.001), PLR (p=0.008), SII (p=0.001), and SIRI (p<0.001) were prognostic factors for progression-free survival (PFS) and overall survival (OS). However, further multivariate Cox regression analysis showed that only SIRI was an independent predictor of PFS and OS (hazard ratio (HR):2.83; 95% confidence interval (CI): 1.561-5.131; p=0.001, HR: 5.19; 95% CI: 2.588-10.406; p<0.001), respectively.ConclusionOur findings indicate that SIRI might be a promising predictive indicator of locally advanced NPC patients.

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Development and Validation of a New Clinical Prognosis Prediction Model for Metabolism in Cancer Patients

10.21203/rs.3.rs-719491/v1 ◽

2021 ◽

Author(s):

Huazhen Tang ◽

Zhenpeng Yang ◽

Xibo Sun ◽

Shuai Lu ◽

Bing Wang ◽

...

Keyword(s):

Prediction Model ◽

Cancer Patients ◽

Cox Regression ◽

Predictive Accuracy ◽

Tumor Stage ◽

Metabolic Reprogramming ◽

Smoking History ◽

Individual Treatment ◽

Clinical Prognosis ◽

Cox Regression Analysis

Abstract Background: Metabolic reprogramming has emerged as an important feature of cancer, and the metabolism-related indexes are closely related to prognosis. Therefore, we develop and verify a large sample clinical prediction model to predict the prognosis in patients with solid tumors.Methods: This retrospective analysis was conducted on a primary cohort of 5006 patients with solid tumor from INSCOC database. A total of 1720 cancer patients treated at the Fujian Cancer Hospital was used to form the validation cohort. A multivariate Cox regression analysis was performed to test the independent significance of different factors and then establish the model. The prediction model was simplified into a nomogram to predict the 1-, 3-and 5-year OS rates. To determine the discriminatory and predictive accuracy capacity of the model, the C-index and calibration curve were evaluated.Results: Multivariate analysis indicated that age, smoking history, tumor stage, tumor metastasis, PGSGA score, FBG, NLR, ALB, TG, and HDL-C were independent factors. Moreover, the nomogram combining the score and clinical parameters can predict patient survival accurately.Conclusions: Clinical indicators based on metabolism reprogramming coould well fit and predict the prognosis of cancer patients, and could provide assistance for the individual treatment of tumor patients in the clinic.

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Prognostic nomogram to predict overall survival for patients with perihilar cholangiocarcinoma: A population-based study of SEER database (Preprint)

10.2196/preprints.26118 ◽

2020 ◽

Author(s):

Chendong Wang

Keyword(s):

Overall Survival ◽

Prognostic Factors ◽

Staging System ◽

Tnm Staging ◽

Survival Outcome ◽

Perihilar Cholangiocarcinoma ◽

Cox Regression Analysis ◽

Calibration Curves ◽

Tnm Staging System ◽

Net Benefits

BACKGROUND Perihilar cholangiocarcinoma (pCCA) is a highly aggressive malignancy with poor prognosis. Accurate prediction is of great significance for patients’ survival outcome. OBJECTIVE The present study aimed to propose a prognostic nomogram for predicting the overall survival (OS) for patients with pCCA. METHODS We conducted a retrospective analysis in a total of 940 patients enrolled from the Surveillance, Epidemiology, and End Results (SEER) program and developed a nomogram based on the prognostic factors identified from the cox regression analysis. Concordance index (C-index), risk group stratification and calibration curves were adopted to test the discrimination and calibration ability of the nomogram with bootstrap method. Decision curves were also plotted to evaluate net benefits in clinical use against TNM staging system. RESULTS On the basis of multivariate analysis, five independent prognostic factors including age, summary stage, surgery, chemotherapy, together with radiation were selected and entered into the nomogram model. The C-index of the model was significantly higher than TNM system in the training set (0.703 vs 0.572, P<0.001), which was also proved in the validation set (0.718 vs 0.588, P<0.001). The calibration curves for 1-, 2-, and 3-year OS probabilities exhibited good agreements between the nomogram-predicted and the actual observation. Decision curves displayed that the nomogram obtained more net benefits than TNM staging system in clinical context. The OS curves of two distinct risk groups stratified by nomogram-predicted survival outcome illustrated statistical difference. CONCLUSIONS We established and validated an easy-to-use prognostic nomogram, which can provide more accurate individualized prediction and assistance in decision making for pCCA patients.

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Circulating cell‐free epstein–barr virus DNA levels and clinical features in Moroccan patients with nasopharyngeal carcinoma

Infectious Agents and Cancer ◽

10.1186/s13027-021-00353-8 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Amina Gihbid ◽

Raja Benzeid ◽

Abdellah Faouzi ◽

Jalal Nourlil ◽

Nezha Tawfiq ◽

...

Keyword(s):

Prognostic Factors ◽

Lymph Node ◽

Nasopharyngeal Carcinoma ◽

Epstein Barr Virus ◽

Disease Stage ◽

Barr Virus ◽

Ebv Dna ◽

Epstein Barr ◽

Pre Treatment ◽

Moroccan Patients

Abstract Background The identification of effective prognosis biomarkers for nasopharyngeal carcinoma (NPC) is crucial to improve treatment and patient outcomes. In the present study, we have attempted to evaluate the correlation between pre-treatment plasmatic Epstein-Barr virus (EBV) DNA load and the conventional prognostic factors in Moroccan patients with NPC. Methods The present study was conducted on 121 histologically confirmed NPC patients, recruited from January 2017 to December 2018. Circulating levels of EBV DNA were measured before therapy initiation using real-time quantitative PCR. Results Overall, undifferentiated non-keratinizingcarcinoma type was the most common histological type (90.1 %), and 61.8 % of patients were diagnosed at an advanced disease stage (IV). Results of pre-treatment plasma EBV load showed that 90.9 % of patients had detectable EBV DNA, with a median plasmatic viral load of 7710 IU/ml. The correlation between pre-treatment EBV DNA load and the conventional prognostic factors showed a significant association with patients’ age (p = 0.01), tumor classification (p = 0.01), lymph node status (p = 0.003), metastasis status (p = 0.00) and overall cancer stage (p = 0.01). Unexpectedly, a significant higher level of pre-treatment EBV DNA was also found in plasma of NPC patients with a family history of cancer (p = 0.04). The risk of NPC mortality in patients with high pretreatment EBVDNA levels was significantly higher than that of those with low pre-treatment plasma EBV-DNA levels (p < 0.05). Furthermore, patients with high pre-treatment EBV-DNA levels (≥ 2000, ≥ 4000) had a significant low overall survival (OS) rates (p < 0.05). Interestingly, lymph node involvement, metastasis status and OS were found to be the most important factors influencing the EBV DNA load in NPC patients. Conclusions The results of the present study clearly showed a high association between pre-treatment EBV DNA load, the crucial classical prognostic factors (T, N, M and disease stage) of NPC and OS, suggesting that pre-treatment EBV DNA can be a useful prognostic biomarker in clinical decision-making and improving NPC treatment in Morocco.

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